scholarly journals MATHEMATICAL ALGORITHM OF FUZZY LOGIC CONTROLLER FOR MULTILEVEL INVERTER CREATING VERTICAL DIVIDED VOLTAGE

2019 ◽  
Vol 59 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Erol Can
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Output Voltage ◽  
Fuzzy Logic Controller ◽  
Boost Converter ◽  
Multilevel Inverter ◽  
Logic Control ◽  
Multi Level

A 9-level inverter with a boost converter has been controlled with a fuzzy logic controller and a PID controller for regulating output voltage applications on resistive (R) and inductive (L), capacitance (C). The mathematical model of this system is created according to the fuzzy logic controlling new high multilevel inverter with a boost converter. The DC-DC boost converter and the multi-level inverter are designed and explained, when creating a mathematical model after a linear pulse width modulation (LPWM), it is preferred to operate the boost multi-level inverter. The fuzzy logic control and the PID control are used to manage the LPWM that allows the switches to operate. The fuzzy logic algorithm is presented by giving necessary mathematical equations that have second-degree differential equations for the fuzzy logic controller. After that, the fuzzy logic controller is set up in the 9-level inverter. The proposed model runs on different membership positions of the triangles at the fuzzy logic controller after testing the PID controller. After the output voltage of the converter, the output voltage of the inverter and the output current of the inverter are observed at the MATLAB SIMULINK, the obtained results are analysed and compared. The results show the demanded performance of the inverter and approve the contribution of the fuzzy logic control on multi-level inverter circuits.

Interval Type-2 Fuzzy Logic Control of Maglev Test Stand

2015 ◽  
Vol 759 ◽  
pp. 71-76
Author(s):  
Ireneusz Dominik
Keyword(s):  
Magnetic Field ◽  
Fuzzy Logic ◽  
Computational Complexity ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Test Stand ◽  
Logic Control ◽  
Interval Type

The paper contains a description of a research into applying classic algorithm of PID controller as well as advanced Type-2 Fuzzy logic controller to ensure stability of the levitating object in magnetic field. The implemented algorithm can handle uncertainties without increasing drastically the computational complexity, which is crucial in case of PLCs. The issues concerning the construction of the unit, where the experiments were carried out, are presented, as well as the characteristics of the object for different controllers.

Fuzzy Logic Controller Sebagai Pengatur Kecepatan Motor Shunt

2017 ◽  
Vol 8 (1) ◽  
pp. 11-16
Author(s):  
Machrus Ali ◽  
Budiman ◽  
Yanuangga Gala Hartlambang ◽  
4 Dwi Ajiatmo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Settling Time ◽  
Logic Control

Telah banyak penelitian pada motor shunt, karena kumparan penguat medan diparalel terhadap kumparan armatur. Motor DC shunt tidak terlalu membutuhkan banyak ruangan karena diameter kawat kecil, tetapi daya keluaran yang dihasilkan kecil karena arus penguatnya kecil. Metode Fuzzy Logic Control (FLC) telah banyak digunakan untuk optimasi suatu system. Penelitian ini membandingkan antara desain tanpa controller, dengan PID controller, dan FLC controller. Dari ketiga desain, menunjukkan bahwa desain control Fuzzy Logic Controller terbaik dari ketiga desain dengan besar putaran 300.0 rpm dengan settling time 1.702 detik dan besar Arus Rotor Motor Shunt (A) sebesar 1.9598 A, dengan setling time 1.323 detik. Penelitian ini akan dikembangkan menggunakan metode kecerdasan buatan lainnya

scholarly journals IMPLEMENTASI PENGONTROLAN TEGANGAN KELUARAN DC STEP UP QUADRATIC BOOST CONVERTER DENGAN VARIASI TEGANGAN INPUT DARI SINGLE PV MENGGUNAKAN LOGIKA FUZZY

Foristek ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Irwan Mahmudi ◽  
Jumiyatun Jumiyatun ◽  
Kadri Kadri
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Output Voltage ◽  
Boost Converter ◽  
Solar Panel ◽  
Solar Panels ◽  
Logic Control ◽  
Fuzzy Logic Method ◽  
Output Side ◽  
Constant Output

Resulting output voltage is not constant due to light intensity and surface temperature of the solar panels. To overcome the output voltage of solar panels that tends to fluctuate, is to add a DC-DC converter to the output side of the DC-DC Converter used in this study is the Quadratic Boost Converter type which has a role to increase the output voltage of the monocrystalline type solar panel so that it remains constant at 24V DC. using Mamdani Fuzzy Logic Control as a method of controlling PWM switching. The results obtained from this study are that the quadratic boost converter can keep the output voltage of the solar panel constant at 24V, with low ripple voltage and overshoot. The Mamdani fuzzy logic method used can produce a constant output voltage value with a rise time of ± 5 seconds. The efficiency obtained from this converter hardware is quite good, ranging from 76% - 88%.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

Comparison of a Fuzzy Logic Controller With a P + D Control Law

1989 ◽  
Vol 111 (2) ◽  
pp. 128-137 ◽  
Author(s):  
S. Daley ◽  
K. F. Gill
Keyword(s):  
Fuzzy Logic ◽  
Attitude Control ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Good Control ◽  
Control Law ◽  
Dynamic Coupling ◽  
Process Knowledge ◽  
Logic Control ◽  
Limited Process

A study is described that compares the performance of a self-organizing fuzzy logic control law (SOC) with that of the more traditional P + D algorithm. The multivariate problem used for the investigation is the attitude control of a flexible satellite that has significant dynamic coupling of the axes. It is demonstrated that the SOC can provide good control, requires limited process knowledge and compares favorably with the P + D algorithm.

A Multilevel Inverter for Photovoltaic Systems With Fuzzy Logic Control

2010 ◽  
Vol 57 (12) ◽  
pp. 4115-4125 ◽  
Author(s):  
Carlo Cecati ◽  
Fabrizio Ciancetta ◽  
Pierluigi Siano
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Multilevel Inverter ◽  
Photovoltaic Systems ◽  
Logic Control

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Fuzzy Logic Control of a Laboratory Magnetic Levitation System

2003 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Piyush Jain ◽  
Devendra P. Garg
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Rate Of Change ◽  
Control Laboratory ◽  
Logic Control ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
And Control

This paper deals with the Fuzzy Logic control of a Magnetic Levitation system [1] available in the Robotics and Control Laboratory at Duke University. The laboratory Magnetic Levitation system primarily consists of a metallic ball, an electromagnet and an infrared optical sensor. The objective of the control experiment is to balance the metallic ball in a magnetic field at a desired position against gravity. The dynamics and control complexity of the system makes it an ideal control laboratory experiment. The student can design their own control schemes and/or change the parameters on the existing control modes supplied with the Magnetic Levitation system, and evaluate and compare their performances. In the process, they overcome challenges such as designing various control techniques, choose which specific control strategy to use, and learn how to optimize it. A Fuzzy Logic control scheme was designed and implemented to control the Magnetic Levitation system. Position and rate of change of position were the inputs to Fuzzy Logic Controller. Experiments were performed on the existing Magnetic Levitation system. Results from these experiments and digital simulation are presented in the paper.

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